Advertisement

Applying Attention Mechanism and Deep Neural Network for Medical Object Segmentation and Classification in X-Ray Fluoroscopy Images

  • Yong Zhang
  • Jun Yan
  • Haitao HuangEmail author
  • Christopher Yencha
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1072)

Abstract

We study how to apply attention mechanism and deep neural network for real-time segmentation and classification of balloon objects from X-ray fluoroscopy images during percutaneous balloon compression (PBC) surgical procedures. Fast and accurate identification of balloon shape and its relative location to the Meckel’s cave can be of significant benefit to the success of the PBC procedure. In this work, we combine the most successful region-based convolutional neural network pipeline with attention mechanism to address these challenges.

Keywords

Percutaneous balloon compression Attention mechanism Deep learning Convolutional neural network 

References

  1. 1.
    Facial Pain Association: FPA Patient Guide: Understanding trigeminal neuralgia and other forms of neuropathic face pain (2019)Google Scholar
  2. 2.
    Adams, H., et al.: Harvey Cushing’s case series of trigeminal neuralgia at the Johns Hopkins Hospital: a surgeon’s quest to advance the treatment of the ‘suicide disease’. Acta Neurochir. 153(5), 1043–1050 (2011)CrossRefGoogle Scholar
  3. 3.
    Prasad, S., Galetta, S.: Trigeminal neuralgia: historical notes and current concepts. Neurologist 15(2), 87–94 (2009)CrossRefGoogle Scholar
  4. 4.
    NINDS, Trigeminal Neuralgia Fact Sheet. National Institute of Neurological Disorders and Stroke (2015)Google Scholar
  5. 5.
    Bendtsen, L., et al.: EAN guideline on trigeminal neuralgia. Eur. J. Neurol. 26, 831–849 (2019)Google Scholar
  6. 6.
    Velagala, J., Mendelson, Z.S., Liu, J.K.: Pain-free outcomes after surgical intervention for trigeminal neuralgia: a comparison of gamma knife and microvascular decompression. Neurosurgery 62(suppl 1), 228 (2015)CrossRefGoogle Scholar
  7. 7.
    Zakrzewska, J.M., Akram, H.: Neurosurgical interventions for the treatment of classical trigeminal neuralgia. Cochrane Database Syst. Rev. 9, CD007312 (2011)Google Scholar
  8. 8.
    Bhargava, D., et al.: TM2-4 Long Term Outcome of Percutaneous Balloon Compression for Trigeminal Neuralgia. BMJ Publishing Group Ltd., London (2019)CrossRefGoogle Scholar
  9. 9.
    Asplund, P.: Percutaneous Balloon Compression for the Treatment of Trigeminal Neuralgia, Umeå universitet (2019)Google Scholar
  10. 10.
    Aydoseli, A., et al.: Neuronavigation-assisted percutaneous balloon compression for the treatment of trigeminal neuralgia: the technique and short-term clinical results. Br. J. Neurosurgery 29, 1–7 (2015)CrossRefGoogle Scholar
  11. 11.
    Georgiopoulos, M., et al.: Minimizing technical failure of percutaneous balloon compression for trigeminal neuralgia using neuronavigation. ISRN Neurol. 2014, 630418 (2014)Google Scholar
  12. 12.
    Trojnik, T., Ŝmigoc, T.: Percutaneous trigeminal ganglion balloon compression rhizotomy: experience in 27 patients. Sci. World J. 2012, 328936 (2012)Google Scholar
  13. 13.
    Vala, H.J., Baxi, A.: A review on Otsu image segmentation algorithm. Int. J. Adv. Res. Comput. Eng. Technol. (IJARCET) 2(2), 387–389 (2013)Google Scholar
  14. 14.
    Wani, M.A., Batchelor, B.G.: Edge-region-based segmentation of range images. IEEE Trans. Pattern Anal. Mach. Intell. 3, 314–319 (1994)CrossRefGoogle Scholar
  15. 15.
    Senthilkumaran, N., Rajesh, R.: Edge detection techniques for image segmentation-a survey of soft computing approaches. Int. J. Recent Trends Eng. 1(2), 250 (2009)Google Scholar
  16. 16.
    Li, C.: Distance regularized level set evolution and its application to image segmentation. IEEE Trans. Image Process. 19(12), 3243–3254 (2010)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Coleman, G.B., Andrews, H.C.: Image segmentation by clustering. Proc. IEEE 67(5), 773–785 (1979)CrossRefGoogle Scholar
  18. 18.
    Sulaiman, S.N., Isa, N.A.M.: Adaptive fuzzy-K-means clustering algorithm for image segmentation. IEEE Trans. Consumer Electron. 56(4), 2661–2668 (2010)CrossRefGoogle Scholar
  19. 19.
    Chuang, K.-S., et al.: Fuzzy c-means clustering with spatial information for image segmentation. Comput. Med. Imaging Graph. 30(1), 9–15 (2006)CrossRefGoogle Scholar
  20. 20.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (2012)Google Scholar
  21. 21.
    Girshick, R., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014)Google Scholar
  22. 22.
    Uijlings, J.R.: Selective search for object recognition. Int. J. Comput. Vis. 104(2), 154–171 (2013)CrossRefGoogle Scholar
  23. 23.
    Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision (2015)Google Scholar
  24. 24.
    Ren, S., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems (2015)Google Scholar
  25. 25.
    He, K., et al.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision (2017)Google Scholar
  26. 26.
    Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: International Conference on Learning Representations, San Diego, CA (2015)Google Scholar
  27. 27.
    Jiang, H., et al.: AHCNet: an application of attention mechanism and hybrid connection for liver tumor segmentation in CT volumes. IEEE Access 7, 24898–24909 (2019)CrossRefGoogle Scholar
  28. 28.
    Wang, W., et al.: Learning unsupervised video object segmentation through visual attention. In: CVPR (2019)Google Scholar
  29. 29.
    Simon, M., Rodner, E., Denzler, J.: Imagenet pre-trained models with batch normalization. arXiv preprint arXiv:1612.01452 (2016)
  30. 30.
    Deng, J., et al.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE (2009)Google Scholar
  31. 31.
    He, K., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)Google Scholar
  32. 32.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)Google Scholar
  33. 33.
    Lin, T.-Y., et al.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)Google Scholar
  34. 34.
    Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)Google Scholar
  35. 35.
    Abdennebi, B., Guenane, L.: Technical considerations and outcome assessment in retrogasserian balloon compression for treatment of trigeminal neuralgia. Series of 901 patients. Surg. Neurol. Int. 5, 118 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Yong Zhang
    • 1
  • Jun Yan
    • 2
  • Haitao Huang
    • 3
    Email author
  • Christopher Yencha
    • 1
  1. 1.Weber State UniversityOgdenUSA
  2. 2.Yidu Cloud Technology Co. Ltd.BeijingChina
  3. 3.The People’s Hospital of Liaoning ProvinceShenyangChina

Personalised recommendations